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利用生物信息学和机器学习方法鉴定 PLA2G1B 重组蛋白在癌症进展中潜在的作用靶点。

Identifying potential targets for preventing cancer progression through the PLA2G1B recombinant protein using bioinformatics and machine learning methods.

机构信息

Department of Respiratory and Critical Care Medicine, The First People's Hospital of Changzhou, The Third Affiliated Hospital of Soochow University, Changzhou 213000, China.

Soochow University, Suzhou 215031, China.

出版信息

Int J Biol Macromol. 2024 Sep;276(Pt 1):133918. doi: 10.1016/j.ijbiomac.2024.133918. Epub 2024 Jul 15.

DOI:10.1016/j.ijbiomac.2024.133918
PMID:39019365
Abstract

Lung cancer is the deadliest and most aggressive malignancy in the world. Preventing cancer is crucial. Therefore, the new molecular targets have laid the foundation for molecular diagnosis and targeted therapy of lung cancer. PLA2G1B plays a key role in lipid metabolism and inflammation. PLA2G1B has selective substrate specificity. In this paper, the recombinant protein molecular structure of PLA2G1B was studied and novel therapeutic interventions were designed to disrupt PLA2G1B activity and impede tumor growth by targeting specific regions or residues in its structure. Construct protein-protein interaction networks and core genes using R's "STRING" program. LASSO, SVM-RFE and RF algorithms identified important genes associated with lung cancer. 282 deg were identified. Enrichment analysis showed that these genes were mainly related to adhesion and neuroactive ligand-receptor interaction pathways. PLA2G1B was subsequently identified as developing a preventative feature. GSEA showed that PLA2G1B is closely related to α-linolenic acid metabolism. Through the analysis of LASSO, SVM-RFE and RF algorithms, we found that PLA2G1B gene may be a preventive gene for lung cancer.

摘要

肺癌是全球最致命和最具侵袭性的恶性肿瘤。预防癌症至关重要。因此,新的分子靶标为肺癌的分子诊断和靶向治疗奠定了基础。PLA2G1B 在脂质代谢和炎症中发挥关键作用。PLA2G1B 具有选择性的底物特异性。本文研究了 PLA2G1B 的重组蛋白分子结构,并设计了新型治疗干预措施,通过针对其结构中的特定区域或残基来破坏 PLA2G1B 的活性并阻碍肿瘤生长。使用 R 的“STRING”程序构建蛋白质-蛋白质相互作用网络和核心基因。LASSO、SVM-RFE 和 RF 算法确定了与肺癌相关的重要基因。确定了 282 个基因。富集分析表明,这些基因主要与粘附和神经活性配体-受体相互作用途径有关。PLA2G1B 随后被确定为具有预防特征。GSEA 表明 PLA2G1B 与α-亚麻酸代谢密切相关。通过 LASSO、SVM-RFE 和 RF 算法的分析,我们发现 PLA2G1B 基因可能是肺癌的预防基因。

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